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Merge pull request #106 from galsci/synchrotron_logpoltens
Synchrotron logpoltens
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docs/preprocess-templates/synchrotron_template_logpoltens.ipynb
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docs/preprocess-templates/utils_synch_generate_map.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,258 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "import healpy as hp\n", | ||
| "from pathlib import Path" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "datadir=Path(\"data/\")\n", | ||
| "output_dir = Path(\"production-data/synch\")\n", | ||
| "output_dir_raw = output_dir / \"raw\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "tags": [ | ||
| "parameters" | ||
| ] | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "output_nside = 8192" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "output_lmax = min(3*output_nside - 1, 8192*2)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "comp = \"synch\"\n", | ||
| "sub = \"template\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Large scales" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "largescale_filename = list(output_dir_raw.glob(f\"{comp}*largescale*{sub}*.fits\"))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "assert len(largescale_filename) == 1" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "largescale_filename = largescale_filename[0]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "largescale_filename" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "alm_log_pol_tens_large_scale = hp.read_alm(largescale_filename, hdu=(1,2,3))" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "map_log_pol_tens_large_scale = hp.alm2map(alm_log_pol_tens_large_scale.astype(np.complex128), nside=output_nside)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Small scales modulation" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "modulate_alm = { k:hp.read_alm(output_dir_raw/f\"synch_{k}_modulation_alms_lmax1535.fits\").astype(np.complex128) for k in [\"temperature\",\"polarization\"] }" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Small scales" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "cl_small_scale = hp.read_cl(output_dir_raw / \"synch_small_scales_logpoltens_cl_lmax16384.fits\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "synalm_lmax = 8192*2 # for reproducibility\n", | ||
| "# synalm_lmax = output_lmax\n", | ||
| "np.random.seed(555)\n", | ||
| "\n", | ||
| "alm_log_pol_tens_small_scale = hp.synalm(\n", | ||
| " list(cl_small_scale) + [np.zeros_like(cl_small_scale[0])] * 3,\n", | ||
| " lmax=synalm_lmax,\n", | ||
| " new=True,\n", | ||
| ")\n", | ||
| "\n", | ||
| "alm_log_pol_tens_small_scale = [hp.almxfl(each, np.ones(3*output_nside-1)) for each in alm_log_pol_tens_small_scale]\n", | ||
| "map_log_pol_tens_small_scale = hp.alm2map(alm_log_pol_tens_small_scale, nside=output_nside)\n", | ||
| "map_log_pol_tens_small_scale[0] *= hp.alm2map(modulate_alm[\"temperature\"], output_nside)\n", | ||
| "map_log_pol_tens_small_scale[1:] *= hp.alm2map(modulate_alm[\"polarization\"], output_nside)\n", | ||
| "assert np.isnan(map_log_pol_tens_small_scale).sum() == 0" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Combine scales\n", | ||
| "\n", | ||
| "* Combine small and large scale maps\n", | ||
| "* Transform from logpoltens to IQU\n", | ||
| "* Write output map" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "map_log_pol_tens = map_log_pol_tens_large_scale + map_log_pol_tens_small_scale" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from pysm3.utils import log_pol_tens_to_map, add_metadata" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "output_map = log_pol_tens_to_map(map_log_pol_tens)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "hp.write_map(output_dir / f\"synch_template_nside{output_nside}.fits\", output_map, dtype=np.float32, overwrite=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "add_metadata([output_dir / f\"synch_template_nside{output_nside}.fits\"], coord=\"G\", unit=\"uK_RJ\", ref_freq=\"23 GHz\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "condanamaster2", | ||
| "language": "python", | ||
| "name": "condanamaster2" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.8.0" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } |
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